This disclosure relates generally to the field of image processing and, more particularly, to various blending techniques for use in generating wide area-of-view images.
One conventional method to generate a wide area-of-view image from a sequence of images (frames) is illustrated in FIG. 1. To begin, a sequence of frames is captured (block 100); frames 1 through 3. The frames are then registered (block 105), identifying the regions of overlap between successive frames (region 110 between frames 1 and 2, and region 115 between frames 2 and 3). Once regions 110 and 115 have been specified, a path or seam is identified through each region (block 120). Here, seam 125 through region 110 and seam 130 through region 115. In accordance with standard scene-cut algorithms, seams 125 and 130 are generally selected to pass through the most similar pixels between each image found in their respective overlap region (i.e., frames 1 and 2 in region 125 and frames 2 and 3 in region 130). As a result, seams 125 and 130 are typically placed outside of, but immediately adjacent to, moving objects within the respective overlap region. With seams 125 and 130 identified, a blend operation across each is performed (block 135); the result of which is final wide area-of-view image 140.
The role of blending operation 135 is to mask or obfuscate the differences between two images. A standard approach to do this uses a process known as “Gradient Domain” blending. Gradient domain blending consists of constructing the gradient field of final image 140 by copying the gradient fields of each image on the corresponding sides of the identified seam (e.g., referring to identifier 145, the gradient fields across seam 125 would be gradient field A from frame 1 and gradient field B from frame 2). Once this is done, the final image is generated by integrating over the gradients across the seam. One popular approach using this technique requires solving Poisson partial differential equations. Reconstructing a final wide angle-of-view image from its gradient field requires substantial computational resources; resources that do not permit the real-time generation of such images on common hand-held devices such as, for example, personal electronic devices having embedded image sensors such as mobile telephones, personal music players, tablet computer systems, and personal gaming devices